code: def stacking(opt):
      lvl_1_ = opt["lvl_1"]()
      lvl_0_ = opt["lvl_0"]()
      top_ = opt["top"]()
  
      stack_lvl_0 = StackingClassifier(
          classifiers=lvl_0_, meta_classifier=top_
      )
      stack_lvl_1 = StackingClassifier(
          classifiers=lvl_1_, meta_classifier=stack_lvl_0
      )
      scores = cross_val_score(stack_lvl_1, X, y, cv=3)
  
      return scores.mean()
  
file_path: Hyperactive/examples/optimization_applications/ensemble_learning_example.py
imports: import itertools
  from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from mlxtend.classifier import StackingClassifier
  from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier, ExtraTreesClassifier
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.linear_model import LogisticRegression
  from sklearn.linear_model import RidgeClassifier
  from hyperactive import Hyperactive
label_desc: Use image node_id 1 for calling a global function with example usage: stacking(opt) and returns: scores